APOE_finemapping
Brian M. Schilder
Most recent update:
2021-04-09
root.dir <- here::here()
knitr::opts_chunk$set(echo = T, root.dir=root.dir)
knitr::opts_knit$set(root.dir=root.dir)
library(dplyr)
library(ggplot2)
library(echolocatoR)Import fine-mapping results
Query fine-mapping portal
Download Alzheimer’s Disease GWAS fine-mapping results for the APOE locus via the echolocatoR Fine-mapping Portal API.
local_finemap <- echolocatoR::GITHUB.portal_query(dataset_types = "GWAS",
phenotypes = "Alzheimer",
LD_panels = c("UKB", "1KGphase3"),
loci = "APOE",
file_types = "multi_finemap",
results_dir = "./data",
overwrite = F)## [1] "Fetching echolocatoR Fine-mapping Portal study metadata."
## [1] "+ 4 datasets remain after filtering."
## [1] "+ Searching for multi_finemap files..."
## [1] "+ 2 unique files identified."
## [1] "+ Downloading 2 files..."
## [1] "+ Returning local file paths."Merge fine-mapping results
finemap_dat <- lapply(local_finemap,function(x){
dataset <- basename(dirname(dirname(dirname(x))))
LD_ref <- stringr::str_split(basename(x),pattern = "[.]")[[1]][2]
printer("Importing",dataset)
dat <- data.table::fread(x)
cbind(dataset=dataset,
LD_ref=LD_ref,
dat)
}) %>% data.table::rbindlist()## [1] "Importing Jansen_2018"
## [1] "Importing Marioni_2018"Identify credible sets
Show just the Union Credible Set SNPs (SNPs nominated by at least 1/4 fine-mapping tools).
ucs_snps <- subset(finemap_dat, Support>0)
createDT(ucs_snps)Identify Consensus SNPs
Filter to just the consensus SNPs (SNPs nominated by 2 or more fine-mapping tools).
consensus_snps <- subset(finemap_dat, Consensus_SNP==T)
createDT(consensus_snps)Cell-type-specific annotations
Epigenomic peak overlap
finemap_dat$Gene<- finemap_dat$Locus
finemap_dat$Locus <- paste(finemap_dat$Locus,finemap_dat$dataset,sep="_")
plot_res <- echolocatoR::SUMMARISE.peak_overlap_plot(merged_DT = finemap_dat,
snp_filter = "Support>0",
include.NOTT_2019_peaks = T,
include.NOTT_2019_enhancers_promoters = T,
include.NOTT_2019_PLACseq = T,
include.CORCES_2020_scATACpeaks = T,
include.CORCES_2020_Cicero_coaccess = T,
include.CORCES_2020_bulkATACpeaks = T,
include.CORCES_2020_HiChIP_FitHiChIP_coaccess = T,
include.CORCES_2020_gene_annotations = T,
plot_celltype_specificity = T,
plot_celltype_specificity_genes = T)## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."
## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."
## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."
## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"
## Error in `[[<-`(`*tmp*`, name, value = "scATAC") :
## 1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"
## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") :
## 1 elements in value to replace 0 elements
Show the epigenomic overlap results
createDT(plot_res$data)Summarise assay counts
Summarise the number of assays in which at least one UCS SNP overlapped with a cell-type-specific epigenomic peak.
celltype_counts <- plot_res$data %>%
dplyr::group_by(Locus, Cell_type) %>%
dplyr::summarise(Count=sum(Count,na.rm = T)) %>%
dplyr::arrange(Locus, desc(Count))## `summarise()` has grouped output by 'Locus'. You can override using the `.groups` argument.createDT(celltype_counts)SNP-level results
The above reports are useful to see thing in aggregate, but here we look at the SNP-level results.
First, we extract cell-type-specific regulatory elements identified in either Nott et al. 2019 or Corces et al. 2020, that overlap with fine-mapped union credible set SNPs (Support>0).
Next, we separately extract cell-type-specific interactome anchors that overlap with those regulatory elements. These can be used to link a given regulatory element (e.g. enhancer) to a specific gene (or set of genes) that it affects.
gr.gre <- SUMMARISE.peak_overlap(merged_DT=finemap_dat,
snp_filter="Support>0",
include.NOTT_2019_peaks=T,
include.NOTT_2019_enhancers_promoters=T,
include.NOTT_2019_PLACseq=T,
include.CORCES_2020_scATACpeaks=T,
include.CORCES_2020_Cicero_coaccess=T,
include.CORCES_2020_bulkATACpeaks=T,
include.CORCES_2020_HiChIP_FitHiChIP_coaccess=T,
include.CORCES_2020_gene_annotations=T) ## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."## 10 query SNP(s) detected with reference overlap.## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."## 16 query SNP(s) detected with reference overlap.## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."## 41 query SNP(s) detected with reference overlap.## 13 query SNP(s) detected with reference overlap.## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"## Start at 2021-04-09 00:32:56## ##
## End at 2021-04-09 00:32:59## Runtime in total is: 3 secs## 0 query SNP(s) detected with reference overlap.## Error in `[[<-`(`*tmp*`, name, value = "scATAC") :
## 1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"## Start at 2021-04-09 00:32:59## ##
## End at 2021-04-09 00:33:00## Runtime in total is: 1 secs## 0 query SNP(s) detected with reference overlap.## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") :
## 1 elements in value to replace 0 elements
## [1] "80 hits across 5 assays in 1 studies found."gr.elements <- subset(gr.gre, !Assay %in% c("HiChIP_FitHiChIP","PLAC"))
non_na_cols <- colnames(gr.elements@elementMetadata)[colSums(!is.na(gr.elements@elementMetadata))>0]
gr.anchors <- subset(gr.gre, Assay %in% c("HiChIP_FitHiChIP","PLAC"))
gr.hits <- clean_granges(GRanges_overlap(GenomicRanges::granges(gr.anchors),
gr.elements[,non_na_cols],
return_merged = F, verbose = T))## [1] "+ dat1 already in GRanges format"
## [1] "+ dat2 already in GRanges format"## 304 query SNP(s) detected with reference overlap.gr.hits$width <- GenomicRanges::width(gr.hits)
unique(gr.elements$Assay)## [1] "H3K27ac" "H3K4me3" "enhancers" "promoters"unique(gr.elements$dataset)## [1] "Jansen_2018" "Marioni_2018"snp_overlap_counts <- data.frame(gr.hits) %>%
dplyr::group_by(Locus, SNP, Cell_type, Consensus_SNP, Support) %>%
dplyr::summarise(Assay_count=dplyr::n_distinct(Assay)) %>%
dplyr::arrange(desc(Consensus_SNP), desc(Support), desc(Assay_count))## `summarise()` has grouped output by 'Locus', 'SNP', 'Cell_type', 'Consensus_SNP'. You can override using the `.groups` argument.createDT(snp_overlap_counts)Gene annotation
Now that we have the hits between fine-mapped SNPs and cell-type-specific regulatory elements, we can try linking those regulatory elements to specific genes.
Fine-mapped SNP overlap with promoters
Find fine-mapped SNPs that fall within regulatory elements (TTS, intron, promoter, promoter-TSS) connected to specific genes (active in one, some, or all cell-types).
promoters_all <- echolocatoR::NOTT_2019.interactome$H3K4me3_around_TSS_annotated_pe
gr.promoters <- GenomicRanges::makeGRangesFromDataFrame(promoters_all,
seqnames.field = "chr",
start.field = "start",
end.field = "end",
keep.extra.columns = T)
gr.overlap1 <- clean_granges(GRanges_overlap(gr.hits, gr.promoters))## 364 query SNP(s) detected with reference overlap.gr.overlap1$Assay <- "interactome_H3K4me3_around_TSS"
gr.overlap1$Element_old <- gr.overlap1$Element
gr.overlap1$Element <- stringr::str_split(gr.overlap1$Annotation," ", simplify = T)[,1]Fine-mapped SNP overlap with enhancers linked to promoters
Find fine-mapped SNPs that fall within enhancers, superenhancers or promoters that interact with promoters (active in one, some or all cell-types).
range_cols <- grep("interactions$", colnames(promoters_all), value = T)
print(paste(length(range_cols),"interacting element columns identified."))## [1] "9 interacting element columns identified."print(range_cols)## [1] "NeuN_enhancer_interactions" "NeuN_promoter_interactions"
## [3] "NeuN_superenhancer_interactions" "PU1_enhancer_interactions"
## [5] "PU1_promoter_interactions" "PU1_superenhancer_interactions"
## [7] "Olig2_enhancer_interactions" "Olig2_promoter_interactions"
## [9] "Olig2_superenhancer_interactions"dat <- data.table::melt.data.table(data.table::data.table(promoters_all),
variable.name = "interaction_type",
measure.vars = range_cols) %>%
tidyr::separate(col = "value", sep =",|, ", into=paste0("range",1:200))## Warning: Expected 200 pieces. Missing pieces filled with `NA` in 53992 rows [1,
## 2, 8, 9, 13, 14, 25, 35, 36, 39, 42, 44, 45, 46, 54, 60, 61, 62, 63, 65, ...].non_na_cols <- colnames(dat)[colSums(!is.na(dat))>0]
promoter_interactions <- dat %>%
dplyr::select(non_na_cols) %>%
data.table::melt.data.table(measure.vars = grep("^range",colnames(.), value = T)
) %>%
subset(!is.na(value) & (value!="")) %>%
tidyr::separate(col = "value", sep="[:]|-", into=c("CHR","START","END")) %>%
tidyr::separate(col = "interaction_type", sep="_", into=c("Marker","Element","Assay"),remove = F) %>%
dplyr::mutate(START=as.numeric(START), END=as.numeric(END),
peak_coordinates=paste0(Chr,":",Start,"-",End)) %>%
dplyr::select(-Chr,-Start,-End) %>%
subset(END>=START)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(non_na_cols)` instead of `non_na_cols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.gr.anchor_bins <- GenomicRanges::makeGRangesFromDataFrame(promoter_interactions,
seqnames.field = "CHR",
start.field = "START",
end.field = "END",
keep.extra.columns = T)
### Convert markers to Celltypes
cell_dict <- list(neurons="NeuN",
microglia="PU1",
oligo="Olig2",
astrocytes="LHX2",
periph="peripheral PU1+")
cell_dict_invert <- setNames(names(cell_dict), cell_dict)
gr.anchor_bins$Cell_type_anchor <- cell_dict_invert[gr.anchor_bins$Marker]
### IMPORTANT!: Return the gr.hits object, not the gr.anchor_bins objects
gr.overlap2 <- GRanges_overlap(gr.anchor_bins[,c("Cell_type_anchor","interaction_type","Element","Gene Name","PeakID","Distance to TSS","Nearest PromoterID","Annotation","Detailed Annotation")],
gr.hits[,c("dataset","Locus","SNP","Support","Consensus_SNP","Cell_type","Assay")],
return_merged = T) %>%
clean_granges() %>%
subset(Cell_type==Cell_type_anchor)## 46894 query SNP(s) detected with reference overlap.## Remove rows that disagree between annotations
# gr.overlap2 <- subset(gr.overlap2, !(Element=="promoters") & (Element.1!="promoter"))
# gr.overlap2$Element_old <- gr.overlap2$Element
# gr.overlap2$Element <- gr.overlap2$Element.1
# gr.overlap2$Assay <- "interactome_anchor_bin"Nominate genes
Nominate causal genes based on the overlap between fine-mapped variants, active regulatory elements, and interactome data.
For robust variant nomination, we find the intersection between the following groups:
- Fine-mapped Consensus SNP AD GWAS variants in the APOE locus (from echolocatoR).
- Coordinates of individual cell-type-specific enhancers/promoters (Table S5 tab 1).
- Coordinates of clusters of enhancers/superenhancers/promoters (Table S5 tab 1).
- Coordinates of individual cell-type-specific interactome anchors (Table S5 tabs 5-12).
- Coordinates of individual promoters/promoter-TSS/TSS/introns with PLAC-seq interactions (Table S5 tab 1).
Table S5 comes from the supplementary material in Nott et al. 2019.
# gr.top_hits <- GRanges_overlap(gr.anchor_bins, gr.promoters)
gr.anchors$Cell_type_interactome <- gr.anchors$Cell_type
interactome_cols <- c("count","fdr","ClusterSize","ClusterType","ClusterNegLog10P",
"Cell_type_interactome","Anchor")
gr.overlap <- GRanges_overlap(gr.anchors[,interactome_cols],
c(gr.overlap1, gr.overlap2),
return_merged = T) %>%
unique() %>%
# Make sure the data overlaps with a celltype in the interactome
subset(!is.na(Cell_type_interactome)) %>%
# make sure celltypes are matching
subset(Cell_type==Cell_type_interactome)## 94020 query SNP(s) detected with reference overlap.regulatory_overlap <- data.frame(gr.overlap) %>%
# subset(Consensus_SNP==T) %>%
dplyr::select(all_of(c("dataset", "Locus","SNP","Support", "Gene.Name",
"Assay","Element","Cell_type", interactome_cols,
"Annotation","PeakID","Distance.to.TSS"
# grep("promoter|enhancer|interactions",
# colnames(gr.overlap@elementMetadata), value = T)
)), -Cell_type_interactome
) %>%
tidyr::separate(col="Locus", into = c("Locus"), sep = "_", extra = "drop") %>%
unique() %>%
# dplyr::arrange(desc(ClusterNegLog10P), ClusterSize) %>%
# dplyr::group_by(dataset, Locus, SNP, Cell_type, Element) %>%
# dplyr::slice_max(order_by = "count", n=1, with_ties = T) %>%
# dplyr::slice_head() %>%
dplyr::arrange(desc(Support)) %>%
dplyr::mutate(Assay=gsub("promoters|enhancers","called_elements", Assay)) %>%
subset(Support>2) %>%
data.table::data.table()
createDT(regulatory_overlap)# regulatory_overlapSession info
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.4 (2021-02-15)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/London
## date 2021-04-09
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## RCircos 1.2.1 2019-03-12 [1] CRAN (R 4.0.2)
## RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.0.2)
## Rcpp 1.0.6 2021-01-15 [1] CRAN (R 4.0.2)
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## readr 1.4.0 2020-10-05 [1] CRAN (R 4.0.2)
## reshape 0.8.8 2018-10-23 [1] CRAN (R 4.0.2)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.0.2)
## Rgraphviz 2.34.0 2020-10-27 [1] Bioconductor
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## rmarkdown 2.7 2021-02-19 [1] CRAN (R 4.0.3)
## rootSolve 1.8.2.1 2020-04-27 [1] CRAN (R 4.0.2)
## rpart 4.1-15 2019-04-12 [1] CRAN (R 4.0.4)
## rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.2)
## Rsamtools 2.6.0 2020-10-27 [1] Bioconductor
## RSQLite 2.2.4 2021-03-12 [1] CRAN (R 4.0.3)
## rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.0.2)
## rtracklayer 1.50.0 2020-10-27 [1] Bioconductor
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## sass 0.3.1 2021-01-24 [1] CRAN (R 4.0.2)
## scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
## stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
## stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
## SummarizedExperiment 1.20.0 2020-10-27 [1] Bioconductor
## supraHex 1.28.1 2020-11-24 [1] Bioconductor
## survival 3.2-7 2020-09-28 [1] CRAN (R 4.0.4)
## tibble 3.1.0 2021-02-25 [1] CRAN (R 4.0.2)
## tidyr 1.1.3 2021-03-03 [1] CRAN (R 4.0.2)
## tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
## utf8 1.2.1 2021-03-12 [1] CRAN (R 4.0.3)
## VariantAnnotation 1.36.0 2020-10-28 [1] Bioconductor
## vctrs 0.3.6 2020-12-17 [1] CRAN (R 4.0.2)
## viridisLite 0.3.0 2018-02-01 [1] CRAN (R 4.0.1)
## withr 2.4.1 2021-01-26 [1] CRAN (R 4.0.2)
## xfun 0.22 2021-03-11 [1] CRAN (R 4.0.3)
## XGR 1.1.7 2020-01-08 [1] url
## XML 3.99-0.6 2021-03-16 [1] CRAN (R 4.0.2)
## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
## XVector 0.30.0 2020-10-28 [1] Bioconductor
## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
## zlibbioc 1.36.0 2020-10-28 [1] Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library